Détail de l'auteur
Auteur Inian Moorthy |
Documents disponibles écrits par cet auteur (3)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesThe use of volunteer geographic information for producing and maintaining authoritative land use and land cover data / Ana-Maria Olteanu-Raimond (2022)
Titre : The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data : EuroSDR and LandSense Workshop, November 24th - 25th 2020, Online Conference Type de document : Actes de congrès Auteurs : Ana-Maria Olteanu-Raimond Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2022 Collection : EuroSDR Workshop report Projets : Landsense / Raimond, Ana-Maria Conférence : VGI4LULC 2020, The use of volunteer geographic information for producing and maintaining authoritative land use and land cover data 24/11/2020 25/11/2020 online OA Proceedings Importance : 40 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] approche participative
[Termes IGN] cartographie collaborative
[Termes IGN] collecte de données
[Termes IGN] Corine Land Cover
[Termes IGN] détection de changement
[Termes IGN] données localisées des bénévoles
[Termes IGN] intégration de données
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] science citoyenne
[Termes IGN] utilisation du solRésumé : (éditeur) The report refers to the workshop that was organized on behalf of EuroSDR and the LandSense project (24-25 November 2020). LandSense aims to build a citizen observatory for Land Use and Land Cover (LULC) monitoring by proposing innovate technologies for data collection, change detection, data quality assessment and offering tools and systems to empower different communities (e.g., private companies, Non Governmental Organisation, National Mapping Agencies, research, public authorities) to monitor and report on LULC. The workshop was co-organized by the LASTIG laboratory of the University Gustave Eiffel and IGN-ENSG, the French National Mapping agency (Ana-Maria Olteanu-Raimond, Clément Mallet, Bénédicte Bucher), the Katholieke Universiteit Leuven (Joep Crompvoets), the International Institute for Applied Systems Analysis (Inian Moorthy) and EuroSDR. Note de contenu : INTRODUCTION GENERALE
1. Introduction
1.1 Land Use and Land Cover data: specificities and challenges
1.2 VGI and citizen science for LULC monitoring
2. Session 1: Use of VGI for LULC data production
2.1 National Land Cover and Land Use Information System of Spain (SIOSE)- Coordination,
production, maintenance and VGI
2.2 A fusion data approach for integrating VGI to update and enrich authoritative LULC data
2.3 OpenStreetMap for Earth Observation (OSM4EO) land use application and benchmark
2.4 Using OpenStreetMap as a data source for training classifiers to generate LULC maps
3. Session 2: Data collection and validation
3.1 A mapping prototype for land use mapping by land users
3.2 A mobile application for collecting snow data in support to satellite remote sensing
3.3 Global land cover monitoring, validation and participation: experiences from several case studies
4. Session 3: Sustainability
4.1 Crowdsourcing reference data collection for land cover and land use mapping: Findings from Picture Pile and FotoquestGo
4.2 Land Cover Monitoring System with Sentinel-Hub and Python Machine Learning Library eo-learn. Is it possible to build a fast and cost-effective LCMS?
4.3 Regular monitoring of landscape changes with Copernicus data- The German land cover change detection service
4.4 Authentication as a Service - A LandSense contribution to improve the FAIR principle in Citizen Science
5. ConclusionNuméro de notice : 28680 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_vgi4lulc.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99973 An experimental framework for integrating citizen and community science into land cover, land use, and land change detection processes in a national mapping agency / Ana-Maria Olteanu-Raimond in Land, vol 7 n° 3 (September 2018)
Titre : An experimental framework for integrating citizen and community science into land cover, land use, and land change detection processes in a national mapping agency Type de document : Article/Communication Auteurs : Ana-Maria Olteanu-Raimond Année de publication : 2018 Projets : Landsense / Raimond, Ana-Maria Article en page(s) : n° 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] cartographie collaborative
[Termes IGN] détection de changement
[Termes IGN] données localisées des bénévoles
[Termes IGN] Institut national de l'information géographique et forestière (France)
[Termes IGN] mise à jour de base de données
[Termes IGN] occupation du sol
[Termes IGN] plateforme collaborative
[Termes IGN] production participative
[Termes IGN] science citoyenne
[Termes IGN] utilisation du solRésumé : (auteur) Accurate and up-to-date information on land use and land cover (LULC) is needed to develop policies on reducing soil sealing through increased urbanization as well as to meet climate targets. More detailed information about building function is also required but is currently lacking. To improve these datasets, the national mapping agency of France, Institut de l’Information Géographique et Foréstière (IGN France), has developed a strategy for updating their LULC database on a update cycle every three years and building information on a continuous cycle using web, mobile, and wiki applications. Developed as part of the LandSense project and eventually tapping into the LandSense federated authentication system, this paper outlines the data collection campaigns, the key concepts that have driven the system architecture, and a description of the technologies developed for this solution. The campaigns have only just begun, so there are only preliminary results to date. Thus far, feedback on the web and mobile applications has been positive, but still requires a further demonstration of feasibility. Numéro de notice : A2018-385 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/land7030103 Date de publication en ligne : 04/09/2018 En ligne : https://doi.org/10.3390/land7030103 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90792
in Land > vol 7 n° 3 (September 2018) . - n° 103[article]
Titre : Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur ; Linda M. See, Auteur ; Steffen Fritz, Auteur ; Inian Moorthy, Auteur ; Christoph Perger, Auteur ; Christian Schill, Auteur ; Doreen S. Boyd, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] cartographie collaborative
[Termes IGN] données localisées des bénévoles
[Termes IGN] modèle de classe latente
[Termes IGN] occupation du sol
[Termes IGN] pondération
[Termes IGN] précision de la classificationRésumé : (Auteur) Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the accuracy with which they can label cases. Here, the potential to increase the accuracy of crowdsourced data on land cover identified from satellite remote sensor images through the use of weighted voting strategies is explored. Critically, the information used to weight contributions based on the accuracy with which a contributor labels cases of a class and the relative abundance of class are inferred entirely from the contributed data only via a latent class analysis. The results show that consensus approaches do yield a classification that is more accurate than that achieved by any individual contributor. Here, the most accurate individual could classify the data with an accuracy of 73.91% while a basic consensus label derived from the data provided by all seven volunteers contributing data was 76.58%. More importantly, the results show that weighting contributions can lead to a statistically significant increase in the overall accuracy to 80.60% by ignoring the contributions from the volunteer adjudged to be the least accurate in labelling. Numéro de notice : A2018-093 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7030080 Date de publication en ligne : 25/02/2018 En ligne : https://doi.org/10.3390/ijgi7030080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89505
in ISPRS International journal of geo-information > vol 7 n° 3 (March 2018)[article]
